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Logging is a means of tracking events that happen when some software runs. The
software’s developer adds logging calls to their code to indicate that certain
events have occurred. An event is described by a descriptive message which can
optionally contain variable data (i.e. data that is potentially different for
each occurrence of the event). Events also have an importance which the
developer ascribes to the event; the importance can also be called the level
or severity.

Logging provides a set of convenience functions for simple logging usage. These
are debug(), info(), warning(), error() and
critical(). To determine when to use logging, see the table below, which
states, for each of a set of common tasks, the best tool to use for it.

Task you want to perform

The best tool for the task

Display console output for ordinary
usage of a command line script or
program

importlogginglogging.warning('Watch out!')# will print a message to the consolelogging.info('I told you so')# will not print anything

If you type these lines into a script and run it, you’ll see:

WARNING:root:Watch out!

printed out on the console. The INFO message doesn’t appear because the
default level is WARNING. The printed message includes the indication of
the level and the description of the event provided in the logging call, i.e.
‘Watch out!’. Don’t worry about the ‘root’ part for now: it will be explained
later. The actual output can be formatted quite flexibly if you need that;
formatting options will also be explained later.

A very common situation is that of recording logging events in a file, so let’s
look at that next. Be sure to try the following in a newly-started Python
interpreter, and don’t just continue from the session described above:

importlogginglogging.basicConfig(filename='example.log',level=logging.DEBUG)logging.debug('This message should go to the log file')logging.info('So should this')logging.warning('And this, too')

And now if we open the file and look at what we have, we should find the log
messages:

The call to basicConfig() should come before any calls to debug(),
info() etc. As it’s intended as a one-off simple configuration facility,
only the first call will actually do anything: subsequent calls are effectively
no-ops.

If you run the above script several times, the messages from successive runs
are appended to the file example.log. If you want each run to start afresh,
not remembering the messages from earlier runs, you can specify the filemode
argument, by changing the call in the above example to:

which is hopefully what you were expecting to see. You can generalize this to
multiple modules, using the pattern in mylib.py. Note that for this simple
usage pattern, you won’t know, by looking in the log file, where in your
application your messages came from, apart from looking at the event
description. If you want to track the location of your messages, you’ll need
to refer to the documentation beyond the tutorial level – see
Advanced Logging Tutorial.

To log variable data, use a format string for the event description message and
append the variable data as arguments. For example:

importlogginglogging.warning('%s before you %s','Look','leap!')

will display:

WARNING:root:Look before you leap!

As you can see, merging of variable data into the event description message
uses the old, %-style of string formatting. This is for backwards
compatibility: the logging package pre-dates newer formatting options such as
str.format() and string.Template. These newer formatting
options are supported, but exploring them is outside the scope of this
tutorial: see Using particular formatting styles throughout your application for more information.

To change the format which is used to display messages, you need to
specify the format you want to use:

importlogginglogging.basicConfig(format='%(levelname)s:%(message)s',level=logging.DEBUG)logging.debug('This message should appear on the console')logging.info('So should this')logging.warning('And this, too')

Notice that the ‘root’ which appeared in earlier examples has disappeared. For
a full set of things that can appear in format strings, you can refer to the
documentation for LogRecord attributes, but for simple usage, you just
need the levelname (severity), message (event description, including
variable data) and perhaps to display when the event occurred. This is
described in the next section.

That concludes the basic tutorial. It should be enough to get you up and
running with logging. There’s a lot more that the logging package offers, but
to get the best out of it, you’ll need to invest a little more of your time in
reading the following sections. If you’re ready for that, grab some of your
favourite beverage and carry on.

If your logging needs are simple, then use the above examples to incorporate
logging into your own scripts, and if you run into problems or don’t
understand something, please post a question on the comp.lang.python Usenet
group (available at http://groups.google.com/group/comp.lang.python) and you
should receive help before too long.

Still here? You can carry on reading the next few sections, which provide a
slightly more advanced/in-depth tutorial than the basic one above. After that,
you can take a look at the Logging Cookbook.

The logging library takes a modular approach and offers several categories
of components: loggers, handlers, filters, and formatters.

Loggers expose the interface that application code directly uses.

Handlers send the log records (created by loggers) to the appropriate
destination.

Filters provide a finer grained facility for determining which log records
to output.

Formatters specify the layout of log records in the final output.

Log event information is passed between loggers, handlers, filters and
formatters in a LogRecord instance.

Logging is performed by calling methods on instances of the Logger
class (hereafter called loggers). Each instance has a name, and they are
conceptually arranged in a namespace hierarchy using dots (periods) as
separators. For example, a logger named ‘scan’ is the parent of loggers
‘scan.text’, ‘scan.html’ and ‘scan.pdf’. Logger names can be anything you want,
and indicate the area of an application in which a logged message originates.

A good convention to use when naming loggers is to use a module-level logger,
in each module which uses logging, named as follows:

logger=logging.getLogger(__name__)

This means that logger names track the package/module hierarchy, and it’s
intuitively obvious where events are logged just from the logger name.

The root of the hierarchy of loggers is called the root logger. That’s the
logger used by the functions debug(), info(), warning(),
error() and critical(), which just call the same-named method of
the root logger. The functions and the methods have the same signatures. The
root logger’s name is printed as ‘root’ in the logged output.

It is, of course, possible to log messages to different destinations. Support
is included in the package for writing log messages to files, HTTP GET/POST
locations, email via SMTP, generic sockets, queues, or OS-specific logging
mechanisms such as syslog or the Windows NT event log. Destinations are served
by handler classes. You can create your own log destination class if
you have special requirements not met by any of the built-in handler classes.

By default, no destination is set for any logging messages. You can specify
a destination (such as console or file) by using basicConfig() as in the
tutorial examples. If you call the functions debug(), info(),
warning(), error() and critical(), they will check to see
if no destination is set; and if one is not set, they will set a destination
of the console (sys.stderr) and a default format for the displayed
message before delegating to the root logger to do the actual message output.

Logger objects have a threefold job. First, they expose several
methods to application code so that applications can log messages at runtime.
Second, logger objects determine which log messages to act upon based upon
severity (the default filtering facility) or filter objects. Third, logger
objects pass along relevant log messages to all interested log handlers.

The most widely used methods on logger objects fall into two categories:
configuration and message sending.

These are the most common configuration methods:

Logger.setLevel() specifies the lowest-severity log message a logger
will handle, where debug is the lowest built-in severity level and critical
is the highest built-in severity. For example, if the severity level is
INFO, the logger will handle only INFO, WARNING, ERROR, and CRITICAL messages
and will ignore DEBUG messages.

You don’t need to always call these methods on every logger you create. See the
last two paragraphs in this section.

With the logger object configured, the following methods create log messages:

Logger.debug(), Logger.info(), Logger.warning(),
Logger.error(), and Logger.critical() all create log records with
a message and a level that corresponds to their respective method names. The
message is actually a format string, which may contain the standard string
substitution syntax of %s, %d, %f, and so on. The
rest of their arguments is a list of objects that correspond with the
substitution fields in the message. With regard to **kwargs, the
logging methods care only about a keyword of exc_info and use it to
determine whether to log exception information.

Logger.log() takes a log level as an explicit argument. This is a
little more verbose for logging messages than using the log level convenience
methods listed above, but this is how to log at custom log levels.

getLogger() returns a reference to a logger instance with the specified
name if it is provided, or root if not. The names are period-separated
hierarchical structures. Multiple calls to getLogger() with the same name
will return a reference to the same logger object. Loggers that are further
down in the hierarchical list are children of loggers higher up in the list.
For example, given a logger with a name of foo, loggers with names of
foo.bar, foo.bar.baz, and foo.bam are all descendants of foo.

Loggers have a concept of effective level. If a level is not explicitly set
on a logger, the level of its parent is used instead as its effective level.
If the parent has no explicit level set, its parent is examined, and so on -
all ancestors are searched until an explicitly set level is found. The root
logger always has an explicit level set (WARNING by default). When deciding
whether to process an event, the effective level of the logger is used to
determine whether the event is passed to the logger’s handlers.

Child loggers propagate messages up to the handlers associated with their
ancestor loggers. Because of this, it is unnecessary to define and configure
handlers for all the loggers an application uses. It is sufficient to
configure handlers for a top-level logger and create child loggers as needed.
(You can, however, turn off propagation by setting the propagate
attribute of a logger to False.)

Handler objects are responsible for dispatching the
appropriate log messages (based on the log messages’ severity) to the handler’s
specified destination. Logger objects can add zero or more handler
objects to themselves with an addHandler() method. As an example
scenario, an application may want to send all log messages to a log file, all
log messages of error or higher to stdout, and all messages of critical to an
email address. This scenario requires three individual handlers where each
handler is responsible for sending messages of a specific severity to a specific
location.

There are very few methods in a handler for application developers to concern
themselves with. The only handler methods that seem relevant for application
developers who are using the built-in handler objects (that is, not creating
custom handlers) are the following configuration methods:

The setLevel() method, just as in logger objects, specifies the
lowest severity that will be dispatched to the appropriate destination. Why
are there two setLevel() methods? The level set in the logger
determines which severity of messages it will pass to its handlers. The level
set in each handler determines which messages that handler will send on.

Application code should not directly instantiate and use instances of
Handler. Instead, the Handler class is a base class that
defines the interface that all handlers should have and establishes some
default behavior that child classes can use (or override).

Formatter objects configure the final order, structure, and contents of the log
message. Unlike the base logging.Handler class, application code may
instantiate formatter classes, although you could likely subclass the formatter
if your application needs special behavior. The constructor takes three
optional arguments – a message format string, a date format string and a style
indicator.

If there is no message format string, the default is to use the
raw message. If there is no date format string, the default date format is:

%Y-%m-%d%H:%M:%S

with the milliseconds tacked on at the end. The style is one of %, ‘{‘
or ‘$’. If one of these is not specified, then ‘%’ will be used.

If the style is ‘%’, the message format string uses
%(<dictionarykey>)s styled string substitution; the possible keys are
documented in LogRecord attributes. If the style is ‘{‘, the message
format string is assumed to be compatible with str.format() (using
keyword arguments), while if the style is ‘$’ then the message format string
should conform to what is expected by string.Template.substitute().

Changed in version 3.2: Added the style parameter.

The following message format string will log the time in a human-readable
format, the severity of the message, and the contents of the message, in that
order:

'%(asctime)s - %(levelname)s - %(message)s'

Formatters use a user-configurable function to convert the creation time of a
record to a tuple. By default, time.localtime() is used; to change this
for a particular formatter instance, set the converter attribute of the
instance to a function with the same signature as time.localtime() or
time.gmtime(). To change it for all formatters, for example if you want
all logging times to be shown in GMT, set the converter attribute in the
Formatter class (to time.gmtime for GMT display).

Creating a logging config file and reading it using the fileConfig()
function.

Creating a dictionary of configuration information and passing it
to the dictConfig() function.

For the reference documentation on the last two options, see
Configuration functions. The following example configures a very simple
logger, a console handler, and a simple formatter using Python code:

You can see that the config file approach has a few advantages over the Python
code approach, mainly separation of configuration and code and the ability of
noncoders to easily modify the logging properties.

Warning

The fileConfig() function takes a default parameter,
disable_existing_loggers, which defaults to True for reasons of
backward compatibility. This may or may not be what you want, since it
will cause any loggers existing before the fileConfig() call to
be disabled unless they (or an ancestor) are explicitly named in the
configuration. Please refer to the reference documentation for more
information, and specify False for this parameter if you wish.

The dictionary passed to dictConfig() can also specify a Boolean
value with key disable_existing_loggers, which if not specified
explicitly in the dictionary also defaults to being interpreted as
True. This leads to the logger-disabling behaviour described above,
which may not be what you want - in which case, provide the key
explicitly with a value of False.

Note that the class names referenced in config files need to be either relative
to the logging module, or absolute values which can be resolved using normal
import mechanisms. Thus, you could use either
WatchedFileHandler (relative to the logging module) or
mypackage.mymodule.MyHandler (for a class defined in package mypackage
and module mymodule, where mypackage is available on the Python import
path).

In Python 3.2, a new means of configuring logging has been introduced, using
dictionaries to hold configuration information. This provides a superset of the
functionality of the config-file-based approach outlined above, and is the
recommended configuration method for new applications and deployments. Because
a Python dictionary is used to hold configuration information, and since you
can populate that dictionary using different means, you have more options for
configuration. For example, you can use a configuration file in JSON format,
or, if you have access to YAML processing functionality, a file in YAML
format, to populate the configuration dictionary. Or, of course, you can
construct the dictionary in Python code, receive it in pickled form over a
socket, or use whatever approach makes sense for your application.

Here’s an example of the same configuration as above, in YAML format for
the new dictionary-based approach:

If no logging configuration is provided, it is possible to have a situation
where a logging event needs to be output, but no handlers can be found to
output the event. The behaviour of the logging package in these
circumstances is dependent on the Python version.

For versions of Python prior to 3.2, the behaviour is as follows:

If logging.raiseExceptions is False (production mode), the event is
silently dropped.

If logging.raiseExceptions is True (development mode), a message
‘No handlers could be found for logger X.Y.Z’ is printed once.

In Python 3.2 and later, the behaviour is as follows:

The event is output using a ‘handler of last resort’, stored in
logging.lastResort. This internal handler is not associated with any
logger, and acts like a StreamHandler which writes the
event description message to the current value of sys.stderr (therefore
respecting any redirections which may be in effect). No formatting is
done on the message - just the bare event description message is printed.
The handler’s level is set to WARNING, so all events at this and
greater severities will be output.

To obtain the pre-3.2 behaviour, logging.lastResort can be set to None.

When developing a library which uses logging, you should take care to
document how the library uses logging - for example, the names of loggers
used. Some consideration also needs to be given to its logging configuration.
If the using application does not use logging, and library code makes logging
calls, then (as described in the previous section) events of severity
WARNING and greater will be printed to sys.stderr. This is regarded as
the best default behaviour.

If for some reason you don’t want these messages printed in the absence of
any logging configuration, you can attach a do-nothing handler to the top-level
logger for your library. This avoids the message being printed, since a handler
will be always be found for the library’s events: it just doesn’t produce any
output. If the library user configures logging for application use, presumably
that configuration will add some handlers, and if levels are suitably
configured then logging calls made in library code will send output to those
handlers, as normal.

A do-nothing handler is included in the logging package:
NullHandler (since Python 3.1). An instance of this handler
could be added to the top-level logger of the logging namespace used by the
library (if you want to prevent your library’s logged events being output to
sys.stderr in the absence of logging configuration). If all logging by a
library foo is done using loggers with names matching ‘foo.x’, ‘foo.x.y’,
etc. then the code:

should have the desired effect. If an organisation produces a number of
libraries, then the logger name specified can be ‘orgname.foo’ rather than
just ‘foo’.

Note

It is strongly advised that you do not add any handlers other
thanNullHandlerto your library’s loggers. This is
because the configuration of handlers is the prerogative of the application
developer who uses your library. The application developer knows their
target audience and what handlers are most appropriate for their
application: if you add handlers ‘under the hood’, you might well interfere
with their ability to carry out unit tests and deliver logs which suit their
requirements.

The numeric values of logging levels are given in the following table. These are
primarily of interest if you want to define your own levels, and need them to
have specific values relative to the predefined levels. If you define a level
with the same numeric value, it overwrites the predefined value; the predefined
name is lost.

Level

Numeric value

CRITICAL

50

ERROR

40

WARNING

30

INFO

20

DEBUG

10

NOTSET

0

Levels can also be associated with loggers, being set either by the developer or
through loading a saved logging configuration. When a logging method is called
on a logger, the logger compares its own level with the level associated with
the method call. If the logger’s level is higher than the method call’s, no
logging message is actually generated. This is the basic mechanism controlling
the verbosity of logging output.

Logging messages are encoded as instances of the LogRecord
class. When a logger decides to actually log an event, a
LogRecord instance is created from the logging message.

Logging messages are subjected to a dispatch mechanism through the use of
handlers, which are instances of subclasses of the Handler
class. Handlers are responsible for ensuring that a logged message (in the form
of a LogRecord) ends up in a particular location (or set of locations)
which is useful for the target audience for that message (such as end users,
support desk staff, system administrators, developers). Handlers are passed
LogRecord instances intended for particular destinations. Each logger
can have zero, one or more handlers associated with it (via the
addHandler() method of Logger). In addition to any
handlers directly associated with a logger, all handlers associated with all
ancestors of the logger are called to dispatch the message (unless the
propagate flag for a logger is set to a false value, at which point the
passing to ancestor handlers stops).

Just as for loggers, handlers can have levels associated with them. A handler’s
level acts as a filter in the same way as a logger’s level does. If a handler
decides to actually dispatch an event, the emit() method is used
to send the message to its destination. Most user-defined subclasses of
Handler will need to override this emit().

Defining your own levels is possible, but should not be necessary, as the
existing levels have been chosen on the basis of practical experience.
However, if you are convinced that you need custom levels, great care should
be exercised when doing this, and it is possibly a very bad idea to define
custom levels if you are developing a library. That’s because if multiple
library authors all define their own custom levels, there is a chance that
the logging output from such multiple libraries used together will be
difficult for the using developer to control and/or interpret, because a
given numeric value might mean different things for different libraries.

MemoryHandler instances send messages to a buffer
in memory, which is flushed whenever specific criteria are met.

HTTPHandler instances send messages to an HTTP
server using either GET or POST semantics.

WatchedFileHandler instances watch the file they are
logging to. If the file changes, it is closed and reopened using the file
name. This handler is only useful on Unix-like systems; Windows does not
support the underlying mechanism used.

NullHandler instances do nothing with error messages. They are used
by library developers who want to use logging, but want to avoid the ‘No
handlers could be found for logger XXX’ message which can be displayed if
the library user has not configured logging. See Configuring Logging for a Library for
more information.

Logged messages are formatted for presentation through instances of the
Formatter class. They are initialized with a format string suitable for
use with the % operator and a dictionary.

For formatting multiple messages in a batch, instances of
BufferingFormatter can be used. In addition to the format
string (which is applied to each message in the batch), there is provision for
header and trailer format strings.

When filtering based on logger level and/or handler level is not enough,
instances of Filter can be added to both Logger and
Handler instances (through their addFilter() method).
Before deciding to process a message further, both loggers and handlers consult
all their filters for permission. If any filter returns a false value, the
message is not processed further.

The basic Filter functionality allows filtering by specific logger
name. If this feature is used, messages sent to the named logger and its
children are allowed through the filter, and all others dropped.

The logging package is designed to swallow exceptions which occur while logging
in production. This is so that errors which occur while handling logging events
- such as logging misconfiguration, network or other similar errors - do not
cause the application using logging to terminate prematurely.

The default implementation of handleError() in Handler
checks to see if a module-level variable, raiseExceptions, is set. If
set, a traceback is printed to sys.stderr. If not set, the exception is
swallowed.

Note

The default value of raiseExceptions is True. This is
because during development, you typically want to be notified of any
exceptions that occur. It’s advised that you set raiseExceptions to
False for production usage.

In the preceding sections and examples, it has been assumed that the message
passed when logging the event is a string. However, this is not the only
possibility. You can pass an arbitrary object as a message, and its
__str__() method will be called when the logging system needs to
convert it to a string representation. In fact, if you want to, you can avoid
computing a string representation altogether - for example, the
SocketHandler emits an event by pickling it and sending it
over the wire.

Formatting of message arguments is deferred until it cannot be avoided.
However, computing the arguments passed to the logging method can also be
expensive, and you may want to avoid doing it if the logger will just throw
away your event. To decide what to do, you can call the
isEnabledFor() method which takes a level argument and returns
true if the event would be created by the Logger for that level of call.
You can write code like this:

so that if the logger’s threshold is set above DEBUG, the calls to
expensive_func1() and expensive_func2() are never made.

There are other optimizations which can be made for specific applications which
need more precise control over what logging information is collected. Here’s a
list of things you can do to avoid processing during logging which you don’t
need:

What you don’t want to collect

How to avoid collecting it

Information about where calls were made from.

Set logging._srcfile to None.

Threading information.

Set logging.logThreads to 0.

Process information.

Set logging.logProcesses to 0.

Also note that the core logging module only includes the basic handlers. If
you don’t import logging.handlers and logging.config, they won’t
take up any memory.